Data matching, also know as record linkage, is a fundamental process for many business applications, including duplicates detection, single view of customer, reporting, master data management, fraud detection, terrorism watch lists, along with many others. Data matching involves linking data records from multiple source systems that do not have a unique key to tie them together. A number of attributes or columns are used in the match process and in general these are text columns.This requires a comprehensive match solution with the following capabilities:

Match data accurately with flexible business rules

Scalability with increasing data volumes

Flexible match options to support real time and batch mode match options

Factors which are critical for an accurate match process:

Ability to handle the variations and other noise in the data records using a variety of fuzzy matching algorithms

Data standardization, such as address, name, and other descriptive fields

External data enhancement of data to fill in missing or changed values

Performance and scalability issues with increasing data volumes:

Match performance on a Multi CPU/Multi Core system falls off exponentially with increasing data volumes

Increasing data volumes lead to increases in hardware and license cost

Longer time for the match process to complete as the data volumes increase